Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)
The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade canno...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2020/3590301 |
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| author | Weiwei Jiang Changhua Lu Yujun Zhang Wei Ju Jizhou Wang Feng Hong Tao Wang Chunsheng Ou |
| author_facet | Weiwei Jiang Changhua Lu Yujun Zhang Wei Ju Jizhou Wang Feng Hong Tao Wang Chunsheng Ou |
| author_sort | Weiwei Jiang |
| collection | DOAJ |
| description | The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy. |
| format | Article |
| id | doaj-art-442fc7ce7a1c4c148011cdb9a91e09ea |
| institution | OA Journals |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-442fc7ce7a1c4c148011cdb9a91e09ea2025-08-20T02:05:51ZengWileyJournal of Spectroscopy2314-49202314-49392020-01-01202010.1155/2020/35903013590301Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)Weiwei Jiang0Changhua Lu1Yujun Zhang2Wei Ju3Jizhou Wang4Feng Hong5Tao Wang6Chunsheng Ou7School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, ChinaAnhui Institute of Optics Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, ChinaSchool of Internet, Anhui University, Hefei 230039, ChinaDepartment of Electronics, Hefei University, Hefei 230061, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, ChinaSchool of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, ChinaThe MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.http://dx.doi.org/10.1155/2020/3590301 |
| spellingShingle | Weiwei Jiang Changhua Lu Yujun Zhang Wei Ju Jizhou Wang Feng Hong Tao Wang Chunsheng Ou Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) Journal of Spectroscopy |
| title | Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) |
| title_full | Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) |
| title_fullStr | Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) |
| title_full_unstemmed | Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) |
| title_short | Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS) |
| title_sort | moving window improved monte carlo uninformative variable elimination combining successive projections algorithm for near infrared spectroscopy nirs |
| url | http://dx.doi.org/10.1155/2020/3590301 |
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